Java Flexibility

Designed to be extended

Easy to use

KeLP supports the definition of applications with a simple and intuitive JSON-based formalism.

What is KeLP?

KeLP makes the implementation of Kernel Functions simple

Kernel-based Learning Platform

KeLP (Kernel-based Learning Platform) is a machine learning platform developed by the SAG group and the ALT group of QCRI. It is entirely written in Java and it is strongly focused on Kernel Machines. It includes different Online and Batch Learning algorithms for classification, regression and clustering. Several kernel functions are already available, ranging from vector-based to structural kernels. KeLP allows for building complex kernel machine based systems, leveraging on the Java language and on a JSON interface to store and load learning configurations, as well as to save the models to be reused.

Learning with Kernels

Exploit the power of kernel functions in popular Machine Learning algorithms

Java based

Exploit the expressivity, and portability of the Java language.

Not only Research

From research laboratories to production environments the step is very small with KeLP.

Open Source

KeLP is released under the Apache 2.0 License, making it open-source and freely-available.

The team behind KeLP

Simone Filice

Applied Scientist at Amazon. Received a PhD in Information Engineering at the University of Roma Tor Vergata in 2016. Research interest in Machine Learning applied to Natural Language Processing Tasks, including Question Answering, Paraphrase Identification and Textual Entailment.

Danilo Croce

KeLP developer

Assistant Professor at the University of Roma, Tor Vergata. His expertise concerns theoretical and applied Machine Learning in the areas of Natural Language Processing and Information Retrieval. He is interested in innovative kernels for advanced syntactic/semantic processing.

Giovanni Da San Martino

Scientist at Qatar Computing Research Institute (HBKU). He received a Ph.D. in Computer Science from the University of Bologna, Italy. His main research interest is machine learning, specifically efficient and adaptive techniques for representing high dimensional data inside learning algorithms.

Scientific Advisors

Associate Professor at the Department of Enterprise Engineering of the University of Roma, Tor Vergata